When conjoint works and when it does not

Conjoint works best when the consumer makes conscious trade-offs between clear attributes. It works poorly in low-involvement categories, with habitual behavior, and when price dominates the decision.

Conjoint analysis is one of the most widely used tools in quantitative market analysis. And that is justified — for the right problem. Conjoint works excellently when the consumer actually weighs product attributes against each other: when they compare different configurations, sizes, features and prices in a purchase situation that requires deliberation.

But conjoint has blind spots. In low-involvement categories — where the purchase is habitual or impulsive — conjoint simulates a deliberative behavior that does not exist in reality. The respondent is forced to think about choices they make on autopilot in the store. That produces data that looks precise but does not reflect actual behavior.

Reflect uses conjoint selectively. In categories with conscious trade-offs (telecom, insurance, automotive, subscriptions) it is a powerful tool. In fast-moving consumer goods we always complement with behavior-based methods. Knowing that the consumer says they prefer X over Y is not the same as knowing they will actually choose X.

Key takeaways

Conjoint works best for conscious, comparative purchase decisions
Low involvement and habitual behavior are poorly captured
The respondent is forced to deliberate about choices made impulsively
High-involvement categories: telecom, insurance, automotive, good for conjoint
FMCG and impulse purchases require complementary behavior-based methods

Example

A fast-food chain used conjoint to optimize its menu. The results pointed to a configuration that nobody actually ordered — because the conjoint design forced rational trade-offs that do not occur at a fast-food counter. A subsequent behavior-based study gave entirely different recommendations.

First choice vs share of preference

First choice shows what the consumer picks first. Share of preference shows how preference is distributed. Which metric is right depends on the category's purchase behavior, and they often give entirely different answers.

Individual level, not aggregate

The average consumer does not exist. Simulation models that work at the individual level capture heterogeneity in preferences and give markedly better forecasts than aggregated models.

How simulation should adapt to category

The same simulation model does not work in all categories. Purchase process, involvement, repertoire behavior and price sensitivity vary, and the simulation model must reflect that reality.

Reflect simulation model

Our simulation model works at the individual level, is calibrated against observed data, and adapts to the category's purchase behavior. The result is forecasts that hold up, not just in the presentation but in the market.

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When conjoint works and when it does not

Key takeaways

Example

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